Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council
Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Eyerich, Kilian [verfasserIn] Brown, Sara J. [verfasserIn] Perez White, Bethany E. [verfasserIn] Tanaka, Reiko J. [verfasserIn] Bissonette, Robert [verfasserIn] Dhar, Sandipan [verfasserIn] Bieber, Thomas [verfasserIn] Hijnen, Dirk J. [verfasserIn] Guttman-Yassky, Emma [verfasserIn] Irvine, Alan [verfasserIn] Thyssen, Jacob P. [verfasserIn] Vestergaard, Christian [verfasserIn] Werfel, Thomas [verfasserIn] Wollenberg, Andreas [verfasserIn] Paller, Amy S. [verfasserIn] Reynolds, Nick J. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: The journal of allergy and clinical immunology - Amsterdam [u.a.] : Elsevier, 1971, 143 |
|---|---|
| Übergeordnetes Werk: |
volume:143 |
| DOI / URN: |
10.1016/j.jaci.2018.10.033 |
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| Katalog-ID: |
ELV001354892 |
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| 520 | |a Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD. | ||
| 650 | 4 | |a Atopic dermatitis | |
| 650 | 4 | |a atopic eczema | |
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| 650 | 4 | |a mechanistic models | |
| 650 | 4 | |a precision medicine | |
| 650 | 4 | |a tissue culture models | |
| 650 | 4 | |a skin equivalents | |
| 650 | 4 | |a systems biology | |
| 700 | 1 | |a Brown, Sara J. |e verfasserin |4 aut | |
| 700 | 1 | |a Perez White, Bethany E. |e verfasserin |4 aut | |
| 700 | 1 | |a Tanaka, Reiko J. |e verfasserin |4 aut | |
| 700 | 1 | |a Bissonette, Robert |e verfasserin |4 aut | |
| 700 | 1 | |a Dhar, Sandipan |e verfasserin |4 aut | |
| 700 | 1 | |a Bieber, Thomas |e verfasserin |4 aut | |
| 700 | 1 | |a Hijnen, Dirk J. |e verfasserin |4 aut | |
| 700 | 1 | |a Guttman-Yassky, Emma |e verfasserin |4 aut | |
| 700 | 1 | |a Irvine, Alan |e verfasserin |4 aut | |
| 700 | 1 | |a Thyssen, Jacob P. |e verfasserin |4 aut | |
| 700 | 1 | |a Vestergaard, Christian |e verfasserin |4 aut | |
| 700 | 1 | |a Werfel, Thomas |e verfasserin |4 aut | |
| 700 | 1 | |a Wollenberg, Andreas |e verfasserin |4 aut | |
| 700 | 1 | |a Paller, Amy S. |e verfasserin |4 aut | |
| 700 | 1 | |a Reynolds, Nick J. |e verfasserin |4 aut | |
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2018 |
| allfields |
10.1016/j.jaci.2018.10.033 doi (DE-627)ELV001354892 (ELSEVIER)S0091-6749(18)31573-2 DE-627 ger DE-627 rda eng 610 DE-600 44.45 bkl 44.78 bkl Eyerich, Kilian verfasserin aut Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD. Atopic dermatitis atopic eczema endotype human models machine learning mechanistic models precision medicine tissue culture models skin equivalents systems biology Brown, Sara J. verfasserin aut Perez White, Bethany E. verfasserin aut Tanaka, Reiko J. verfasserin aut Bissonette, Robert verfasserin aut Dhar, Sandipan verfasserin aut Bieber, Thomas verfasserin aut Hijnen, Dirk J. verfasserin aut Guttman-Yassky, Emma verfasserin aut Irvine, Alan verfasserin aut Thyssen, Jacob P. verfasserin aut Vestergaard, Christian verfasserin aut Werfel, Thomas verfasserin aut Wollenberg, Andreas verfasserin aut Paller, Amy S. verfasserin aut Reynolds, Nick J. verfasserin aut Enthalten in The journal of allergy and clinical immunology Amsterdam [u.a.] : Elsevier, 1971 143 Online-Ressource (DE-627)32045553X (DE-600)2006613-2 (DE-576)094478864 1097-6825 nnns volume:143 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_168 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 44.45 Immunologie 44.78 Immunkrankheiten AR 143 |
| spelling |
10.1016/j.jaci.2018.10.033 doi (DE-627)ELV001354892 (ELSEVIER)S0091-6749(18)31573-2 DE-627 ger DE-627 rda eng 610 DE-600 44.45 bkl 44.78 bkl Eyerich, Kilian verfasserin aut Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD. Atopic dermatitis atopic eczema endotype human models machine learning mechanistic models precision medicine tissue culture models skin equivalents systems biology Brown, Sara J. verfasserin aut Perez White, Bethany E. verfasserin aut Tanaka, Reiko J. verfasserin aut Bissonette, Robert verfasserin aut Dhar, Sandipan verfasserin aut Bieber, Thomas verfasserin aut Hijnen, Dirk J. verfasserin aut Guttman-Yassky, Emma verfasserin aut Irvine, Alan verfasserin aut Thyssen, Jacob P. verfasserin aut Vestergaard, Christian verfasserin aut Werfel, Thomas verfasserin aut Wollenberg, Andreas verfasserin aut Paller, Amy S. verfasserin aut Reynolds, Nick J. verfasserin aut Enthalten in The journal of allergy and clinical immunology Amsterdam [u.a.] : Elsevier, 1971 143 Online-Ressource (DE-627)32045553X (DE-600)2006613-2 (DE-576)094478864 1097-6825 nnns volume:143 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_168 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 44.45 Immunologie 44.78 Immunkrankheiten AR 143 |
| allfields_unstemmed |
10.1016/j.jaci.2018.10.033 doi (DE-627)ELV001354892 (ELSEVIER)S0091-6749(18)31573-2 DE-627 ger DE-627 rda eng 610 DE-600 44.45 bkl 44.78 bkl Eyerich, Kilian verfasserin aut Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD. Atopic dermatitis atopic eczema endotype human models machine learning mechanistic models precision medicine tissue culture models skin equivalents systems biology Brown, Sara J. verfasserin aut Perez White, Bethany E. verfasserin aut Tanaka, Reiko J. verfasserin aut Bissonette, Robert verfasserin aut Dhar, Sandipan verfasserin aut Bieber, Thomas verfasserin aut Hijnen, Dirk J. verfasserin aut Guttman-Yassky, Emma verfasserin aut Irvine, Alan verfasserin aut Thyssen, Jacob P. verfasserin aut Vestergaard, Christian verfasserin aut Werfel, Thomas verfasserin aut Wollenberg, Andreas verfasserin aut Paller, Amy S. verfasserin aut Reynolds, Nick J. verfasserin aut Enthalten in The journal of allergy and clinical immunology Amsterdam [u.a.] : Elsevier, 1971 143 Online-Ressource (DE-627)32045553X (DE-600)2006613-2 (DE-576)094478864 1097-6825 nnns volume:143 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_168 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 44.45 Immunologie 44.78 Immunkrankheiten AR 143 |
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10.1016/j.jaci.2018.10.033 doi (DE-627)ELV001354892 (ELSEVIER)S0091-6749(18)31573-2 DE-627 ger DE-627 rda eng 610 DE-600 44.45 bkl 44.78 bkl Eyerich, Kilian verfasserin aut Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD. Atopic dermatitis atopic eczema endotype human models machine learning mechanistic models precision medicine tissue culture models skin equivalents systems biology Brown, Sara J. verfasserin aut Perez White, Bethany E. verfasserin aut Tanaka, Reiko J. verfasserin aut Bissonette, Robert verfasserin aut Dhar, Sandipan verfasserin aut Bieber, Thomas verfasserin aut Hijnen, Dirk J. verfasserin aut Guttman-Yassky, Emma verfasserin aut Irvine, Alan verfasserin aut Thyssen, Jacob P. verfasserin aut Vestergaard, Christian verfasserin aut Werfel, Thomas verfasserin aut Wollenberg, Andreas verfasserin aut Paller, Amy S. verfasserin aut Reynolds, Nick J. verfasserin aut Enthalten in The journal of allergy and clinical immunology Amsterdam [u.a.] : Elsevier, 1971 143 Online-Ressource (DE-627)32045553X (DE-600)2006613-2 (DE-576)094478864 1097-6825 nnns volume:143 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_168 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 44.45 Immunologie 44.78 Immunkrankheiten AR 143 |
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10.1016/j.jaci.2018.10.033 doi (DE-627)ELV001354892 (ELSEVIER)S0091-6749(18)31573-2 DE-627 ger DE-627 rda eng 610 DE-600 44.45 bkl 44.78 bkl Eyerich, Kilian verfasserin aut Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD. Atopic dermatitis atopic eczema endotype human models machine learning mechanistic models precision medicine tissue culture models skin equivalents systems biology Brown, Sara J. verfasserin aut Perez White, Bethany E. verfasserin aut Tanaka, Reiko J. verfasserin aut Bissonette, Robert verfasserin aut Dhar, Sandipan verfasserin aut Bieber, Thomas verfasserin aut Hijnen, Dirk J. verfasserin aut Guttman-Yassky, Emma verfasserin aut Irvine, Alan verfasserin aut Thyssen, Jacob P. verfasserin aut Vestergaard, Christian verfasserin aut Werfel, Thomas verfasserin aut Wollenberg, Andreas verfasserin aut Paller, Amy S. verfasserin aut Reynolds, Nick J. verfasserin aut Enthalten in The journal of allergy and clinical immunology Amsterdam [u.a.] : Elsevier, 1971 143 Online-Ressource (DE-627)32045553X (DE-600)2006613-2 (DE-576)094478864 1097-6825 nnns volume:143 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_168 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 44.45 Immunologie 44.78 Immunkrankheiten AR 143 |
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Eyerich, Kilian @@aut@@ Brown, Sara J. @@aut@@ Perez White, Bethany E. @@aut@@ Tanaka, Reiko J. @@aut@@ Bissonette, Robert @@aut@@ Dhar, Sandipan @@aut@@ Bieber, Thomas @@aut@@ Hijnen, Dirk J. @@aut@@ Guttman-Yassky, Emma @@aut@@ Irvine, Alan @@aut@@ Thyssen, Jacob P. @@aut@@ Vestergaard, Christian @@aut@@ Werfel, Thomas @@aut@@ Wollenberg, Andreas @@aut@@ Paller, Amy S. @@aut@@ Reynolds, Nick J. @@aut@@ |
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2018-01-01T00:00:00Z |
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610 DE-600 44.45 bkl 44.78 bkl Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council Atopic dermatitis atopic eczema endotype human models machine learning mechanistic models precision medicine tissue culture models skin equivalents systems biology |
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Eyerich, Kilian Brown, Sara J. Perez White, Bethany E. Tanaka, Reiko J. Bissonette, Robert Dhar, Sandipan Bieber, Thomas Hijnen, Dirk J. Guttman-Yassky, Emma Irvine, Alan Thyssen, Jacob P. Vestergaard, Christian Werfel, Thomas Wollenberg, Andreas Paller, Amy S. Reynolds, Nick J. |
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human and computational models of atopic dermatitis: a review and perspectives by an expert panel of the international eczema council |
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Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council |
| abstract |
Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD. |
| abstractGer |
Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD. |
| abstract_unstemmed |
Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD. |
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Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. 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| score |
7.399188 |